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  A Neural Algorithm of Artistic Style

Gatys, L., Ecker, A., & Bethge, M. (2016). A Neural Algorithm of Artistic Style. Poster presented at 16th Annual Meeting of the Vision Sciences Society (VSS 2016), St. Pete Beach, FL, USA.

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Gatys, LA1, Author
Ecker, AS1, Author           
Bethge, M1, Author           
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1Werner Reichardt Centre for Integrative Neuroscience and Institute of Theoretical Physics, University of Tübingen, Germany, ou_persistent22              

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 Abstract: In fine art, especially painting, humans have mastered the skill to create unique visual experiences by composing a complex interplay between the content and style of an image. The algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. Recently, a class of biologically inspired vision models called Deep Neural Networks have demonstrated near-human performance in complex visual tasks such as object and face recognition. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system can separate and recombine the content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. In light of recent studies using fMRI and electrophysiology that have shown striking similarities between performance-optimised artificial neural networks and biological vision, our work offers a path towards an algorithmic understanding of how humans create and perceive artistic imagery. The algorithm introduces a novel class of stimuli that could be used to test specific computational hypotheses about the perceptual processing of artistic style.

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 Dates: 2016-08
 Publication Status: Issued
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 Identifiers: DOI: 10.1167/16.12.326
BibTex Citekey: GatysEB2016
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Title: 16th Annual Meeting of the Vision Sciences Society (VSS 2016)
Place of Event: St. Pete Beach, FL, USA
Start-/End Date: 2016-05-13 - 2016-05-18

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Title: Journal of Vision
Source Genre: Journal
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Publ. Info: Charlottesville, VA : Scholar One, Inc.
Pages: - Volume / Issue: 16 (12) Sequence Number: - Start / End Page: 326 Identifier: ISSN: 1534-7362
CoNE: https://pure.mpg.de/cone/journals/resource/111061245811050